CL_Analysis_All

1 Import simulations results and screening data

We study the clinical value of some logical models with cell lines data from CellModelPassports portal and GDSC dataset. Let’s first import simulation results and drug/CRISPR screening files.

## [1] "All imports OK"

2 Clinical characterization of cell lines

2.1 Different screenings and metrics

But first, is there any consistence between all these values?

3 Data reprocessing

We define a normalised variable based on level without any drug inhibition (i.e \(Proliferation_{normalised} = Proliferation_{withDrug} / Proliferation_{withoutDrug}\))

4 Simulations: a first quantitative approach with correlations

Some interesting points:

• We observe very significant correlations for both drug and CRISPR screening
• Normalised proliferation appeared as the best proxy for drug sensitivity
• RNA alone is usually not significant but its conjunction with mutation usually results in better results

Here are additional plots for other targets;

For drugs we will focus on PLX (but very similar to PLX in all aspects, no particular criterion to distinguish) and AUC metric (less sensitive to extrapolation). For CRISPR screening we will focus on CC2 dataset, more balanced in CM and CRC. For output we will also focus on normalisedx Proliferation scores:

Here is the pruned version of the plot for publication:

5 Explore the results with scatter plots

5.1 Simple plots

Now we want to have a deeper understanding of these correlation relations looking at the scatter plots

Here is the version for publication:

And here is the version with table

Additional plot for p53 and PI3K:

5.2 Interactive plots

We can have a deeper look at scatter plot with interactive settings

Here is the non-interactive reference plots

Let’s generate each column as an interactive plot, first with drugs and then with CRISPR:

And a last interactive plot to visualize the benefit of RNA addition for CRISPR prediction: